U.S. patent number 10,776,795 [Application Number 15/429,997] was granted by the patent office on 2020-09-15 for data amelioration and reformation system.
This patent grant is currently assigned to AMERICAN EXPRESS TRAVEL RELATED SERVICES COMPANY, INC.. The grantee listed for this patent is American Express Travel Related Services Company, Inc.. Invention is credited to Jane E. Cook, Michael Alexander Gonzales, Amy L. Harbour, Sachin Jadhav, Yogaraj Jeyaprakasam, Deepak Narayanan, Michael A. Woods.
![](/patent/grant/10776795/US10776795-20200915-D00000.png)
![](/patent/grant/10776795/US10776795-20200915-D00001.png)
![](/patent/grant/10776795/US10776795-20200915-D00002.png)
![](/patent/grant/10776795/US10776795-20200915-D00003.png)
![](/patent/grant/10776795/US10776795-20200915-D00004.png)
United States Patent |
10,776,795 |
Harbour , et al. |
September 15, 2020 |
Data amelioration and reformation system
Abstract
The systems may include receiving, by a processor, transaction
information for a transaction, wherein the transaction information
comprises a transaction amount; matching, by the processor, the
transaction information with a transaction type; retrieving, by the
processor, a plurality of possible charge types associated with the
transaction type; comparing, by the processor, the transaction
information with the plurality of possible charge types;
separating, by the processor, the transaction amount of the
transaction information into at least one individual charge amount;
and/or identifying, by the processor, a charge type of the
plurality of possible charge types associated with the at least one
individual charge amount.
Inventors: |
Harbour; Amy L. (Huntington,
WV), Woods; Michael A. (Cypress, TX), Cook; Jane E.
(Mountain Lakes, NJ), Gonzales; Michael Alexander (Fulshear,
TX), Jadhav; Sachin (Phoenix, AZ), Jeyaprakasam;
Yogaraj (Phoenix, AZ), Narayanan; Deepak (Peoria,
AZ) |
Applicant: |
Name |
City |
State |
Country |
Type |
American Express Travel Related Services Company, Inc. |
New York |
NY |
US |
|
|
Assignee: |
AMERICAN EXPRESS TRAVEL RELATED
SERVICES COMPANY, INC. (New York, NY)
|
Family
ID: |
1000005055859 |
Appl.
No.: |
15/429,997 |
Filed: |
February 10, 2017 |
Prior Publication Data
|
|
|
|
Document
Identifier |
Publication Date |
|
US 20180232730 A1 |
Aug 16, 2018 |
|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06Q
30/0201 (20130101) |
Current International
Class: |
G06Q
30/02 (20120101) |
References Cited
[Referenced By]
U.S. Patent Documents
Primary Examiner: King; Joseph W.
Assistant Examiner: Patel; Amit
Attorney, Agent or Firm: Thomas Horstemeyer, LLP
Claims
What is claimed is:
1. A method, comprising: receiving, by a processor, transaction
information for a transaction, wherein the transaction information
comprises a transaction amount comprising a primary charge amount
and an ancillary charge amount; determining, by the processor, a
first distance between the transaction information and enhanced
market information; associating, by the processor and based on
matching rules, the transaction information with a transaction type
based on the first distance that is closest between the transaction
information and the enhanced market information, wherein the
enhanced market information having the first distance that is the
closest includes the transaction type; retrieving, by the processor
and from the enhanced market information, a plurality of possible
charge types associated with the transaction type; comparing, by
the processor, the transaction information with the plurality of
possible charge types; determining, by the processor, a second
distance between the transaction information and the plurality of
possible charge types; determining, by the processor and based on
the second distance, a charge type of the plurality of possible
charge types in the transaction information; identifying, by the
processor, the ancillary charge amount in the transaction amount
based upon the charge type and the transaction type; separating, by
the processor, the ancillary charge amount from the transaction
amount; and sending, by the processor, the ancillary charge and the
transaction amount to a display of a data output configured to
dynamically relocate obscured information of an underlying window
by: displaying a first window containing the ancillary charge and
the transaction amount within a graphical user interface;
displaying a second window within the graphical user interface;
constantly monitoring boundaries of the first window and the second
window to detect an overlap condition where the second window
overlaps the first window such that the ancillary charge and the
transaction amount in the first window is obscured from a user's
view; determining that the ancillary charge and the transaction
amount would not be completely viewable if relocated to an
unobstructed portion of the first window; calculating a first
measure of the area of the first window and a second measure of the
area of the unobstructed portion of the first window; calculating a
scaling factor which is proportional to the difference between the
first measure and the second measure; scaling the ancillary charge
and the transaction amount based upon the scaling factor;
automatically relocating the scaled ancillary charge and the scaled
transaction amount to the unobscured portion of the first window in
a second format during an overlap condition so that the entire
scaled ancillary charge and scaled transaction amount is viewable
by the user; and automatically returning the relocated scaled
ancillary charge and scaled transaction amount to the first format
within the first window when the overlap condition no longer
exists.
2. The method of claim 1, further comprising collecting, by the
processor, market information from a data source to create the
enhanced market information, wherein the data source is at least
one of a global distribution system, a business travel account
associated with a merchant or consumer, a consumer transaction
history associated with a consumer profile, a merchant transaction
history associated with a merchant profile, or a purchase policy
associated with the consumer or the merchant.
3. The method of claim 2, further comprising matching and
enriching, by the processor, market information to create the
enhanced market information.
4. The method of claim 1, wherein the plurality of possible charge
types is associated with at least one of a consumer or a
merchant.
5. The method of claim 1, further comprising scoring, by the
processor, the enhanced market information based on the first
distance between the transaction information and the enhanced
market information.
6. The method of claim 1, further comprising adjusting, by the
processor, the matching rules based on a change in transaction.
7. The method of claim 1, further comprising: ranking, by the
processor, data sources of market information by quality of data;
replacing, by the processor, data in the data sources wherein the
quality of data is of lower quality; and using, by the processor,
the data sources with the quality of data that is of higher
quality.
8. An article of manufacture including a non-transitory, tangible
computer readable storage medium having instructions stored thereon
that, in response to execution by a processor, cause the processor
to perform operations comprising: receiving, by the processor,
transaction information for a transaction, wherein the transaction
information comprises a transaction amount comprising a primary
charge amount and an ancillary charge amount; determining, by the
processor, a first distance between the transaction information and
enhanced market information; associating, by the processor and
based on matching rules, the transaction information with a
transaction type based on the first distance that is closest
between the transaction information and the enhanced market
information, wherein the enhanced market information having the
first distance that is the closest includes the transaction type;
retrieving, by the processor and from the enhanced market
information, a plurality of possible charge types associated with
the transaction type; comparing, by the processor, the transaction
information with the plurality of possible charge types;
determining, by the processor, a second distance between the
transaction information and the plurality of possible charge types;
determining, by the processor and based on the second distance, a
charge type of the plurality of possible charge types in the
transaction information; identifying, by the processor, the
ancillary charge amount in the transaction amount; associating, by
the processor, the ancillary charge amount with the charge type;
separating, by the processor, the ancillary charge amount from the
transaction amount; and sending, by the processor, the ancillary
charge and the transaction amount to a display of a data output
configured to dynamically relocate obscured information of an
underlying window by: displaying a first window containing the
ancillary charge and the transaction amount within a graphical user
interface; displaying a second window within the graphical user
interface; constantly monitoring boundaries of the first window and
the second window to detect an overlap condition where the second
window overlaps the first window such that the ancillary charge and
the transaction amount in the first window is obscured from a
user's view; determining that the ancillary charge and the
transaction amount would not be completely viewable if relocated to
an unobstructed portion of the first window; calculating a first
measure of the area of the first window and a second measure of the
area of the unobstructed portion of the first window; calculating a
scaling factor which is proportional to the difference between the
first measure and the second measure; scaling the ancillary charge
and the transaction amount based upon the scaling factor;
automatically relocating the scaled ancillary charge and the scaled
transaction amount to the unobscured portion of the first window in
a second format during an overlap condition so that the entire
scaled ancillary charge and scaled transaction amount is viewable
by the user; and automatically returning the relocated scaled
ancillary charge and scaled transaction amount to the first format
within the first window when the overlap condition no longer
exists.
9. The article of claim 8, further comprising collecting, by the
computer-based system, market information from a data source to
create the enhanced market information, wherein the data source is
at least one of a global distribution system, a business travel
account associated with a merchant or consumer, a consumer
transaction history associated with a consumer profile, a merchant
transaction history associated with a merchant profile, or a
purchase policy associated with the consumer or the merchant.
10. The article of claim 9, further comprising matching and
enriching, by the processor, market information to create the
enhanced market information.
11. The article of claim 8, wherein the plurality of possible
charge types is associated with at least one of a consumer or a
merchant.
12. The article of claim 8, further comprising scoring, by the
processor, the enhanced market information based on the first
distance between the transaction information and the enhanced
market information.
13. The article of claim 8, further comprising adjusting, by the
processor, the matching rules based on a change in transaction
behavior.
14. The article of claim 8, further comprising: ranking, by the
processor, data sources of market information by quality of data;
replacing, by the processor, data in the data sources wherein the
quality of data is of lower quality; and using, by the processor,
the data sources with the quality of data that is of higher
quality.
15. A system comprising: A processor: and a tangible,
non-transitory memory configured to communicate with the processor,
the tangible, non-transitory memory having instructions stored
thereon that, in response to execution by the processor, cause the
processor to perform operations comprising: receiving, by the
processor, transaction information for a transaction, wherein the
transaction information comprises a transaction amount comprising a
primary charge amount and an ancillary charge amount; determining,
by the processor, a first distance between the transaction
information and enhanced market information; associating, by the
processor and based on matching rules, the transaction information
with a transaction type based on the first distance that is closest
between the transaction information and the enhanced market
information, wherein the enhanced market information having the
first distance that is the closest includes the transaction type;
retrieving, by the processor and from the enhanced market
information, a plurality of possible charge types associated with
the transaction type; comparing, by the processor, the transaction
information with the plurality of possible charge types;
determining, by the processor, a second distance between the
transaction information and the plurality of possible charge types;
determining, by the processor and based on the second distance, a
charge type of the plurality of possible charge types in the
transaction information; identifying, by the processor, the
ancillary charge amount in the transaction amount; associating, by
the processor, the ancillary charge amount with the charge type;
separating, by the processor, the ancillary charge amount from the
transaction amount; and sending, by the processor, the ancillary
charge and the transaction amount to a display of a data output
configured to dynamically relocate obscured information of an
underlying window by: displaying a first window containing the
ancillary charge and the transaction amount within a graphical user
interface; displaying a second window within the graphical user
interface; constantly monitoring boundaries of the first window and
the second window to detect an overlap condition where the second
window overlaps the first window such that the ancillary charge and
the transaction amount in the first window is obscured from a
user's view; determining that the ancillary charge and the
transaction amount would not be completely viewable if relocated to
an unobstructed portion of the first window; calculating a first
measure of the area of the first window and a second measure of the
area of the unobstructed portion of the first window; calculating a
scaling factor which is proportional to the difference between the
first measure and the second measure; scaling the ancillary charge
and the transaction amount based upon the scaling factor;
automatically relocating the scaled ancillary charge and the scaled
transaction amount to the unobscured portion of the first window in
a second format during an overlap condition so that the entire
scaled ancillary charge and scaled transaction amount is viewable
by the user; and automatically returning the relocated scaled
ancillary charge and scaled transaction amount to the first format
within the first window when the overlap condition no longer
exists.
16. The system of claim 15, further comprising collecting, by the
processor, market information from a data source to create the
enhanced market information, wherein the data source is at least
one of a global distribution system, a business travel account
associated with a merchant or a consumer, a consumer transaction
history associated with a consumer profile, a merchant transaction
history associated with a merchant profile, or a purchase policy
associated with the consumer or the merchant.
17. The system of claim 15, further comprising matching and
enriching, by the processor, market information to create the
enhanced market information.
18. The system of claim 15, wherein the plurality of possible
charge types is associated with at least one of a consumer or a
merchant.
19. The system of claim 15, further comprising adjusting, by the
processor, the matching rules based on a change in transaction
behavior.
20. The system of claim 15, further comprising: ranking, by the
processor, data sources of market information by quality of data;
replacing, by the processor, data in the data sources wherein the
quality of data is of lower quality; and using, by the processor,
the data sources with the quality of data that is of higher
quality.
Description
FIELD
The present disclosure generally relates to data amelioration and
reformation.
BACKGROUND
Various transactions (e.g., purchasing a travel ticket, renting a
car or hotel, etc.) may have a primary charge associated with the
price of the purchase or rental, and one or more ancillary charges
which the consumer must pay to complete the transaction. Records of
charge for such transactions may show the total amount for the
transaction, but may not show the specific amounts of any ancillary
charges.
SUMMARY
A system, method, and article of manufacture (collectively, "the
system") are disclosed relating to data amelioration and
reformation. In various embodiments, the system may be configured
to perform operations including receiving, by a processor,
transaction information for a transaction, wherein the transaction
information comprises a transaction amount; matching, by the
processor, the transaction information with a transaction type;
retrieving, by the processor, a plurality of possible charge types
associated with the transaction type; comparing, by the processor,
the transaction information with the plurality of possible charge
types; identifying, by the processor, at least one individual
charge amount comprised in the transaction amount; and/or
separating, by the processor, the transaction amount into the at
least one individual charge amount. In various embodiments, the
method may further comprise collecting, by the processor, market
information from a data source. The data source may be at least one
of a global distribution system, a business travel account
associated with a merchant or consumer, a transaction history
associated with a consumer profile, a transaction history
associated with a merchant profile, or a purchase policy associated
with a consumer or merchant. In various embodiments, the method may
further comprise matching and enriching, by the processor, the
market information to create enhanced market information, wherein
the plurality of possible charge types comes from the enhanced
market information.
In various embodiments, the plurality of possible charge types may
be associated with at least one of a consumer or a merchant. The
transaction amount comprises a primary charge and an ancillary
charge. The separating the transaction amount into the at least one
individual charge amount may comprise separating the transaction
amount into the primary charge and the ancillary charge. The
identifying the charge type associated with the at least one
individual charge amount may comprise identifying the primary
charge and the ancillary charge.
BRIEF DESCRIPTION OF THE DRAWINGS
The subject matter of the present disclosure is particularly
pointed out and distinctly claimed in the concluding portion of the
specification. A more complete understanding of the present
disclosure, however, may best be obtained by referring to the
detailed description and claims when considered in connection with
the drawing figures.
FIG. 1 shows a system for data amelioration and reformation, in
accordance with various embodiments;
FIG. 2A shows an exemplary matching engine, in accordance with
various embodiments;
FIG. 2B shows an exemplary monitoring system, in accordance with
various embodiments; and
FIG. 3 shows a flowchart depicting an exemplary method for
separating a transaction amount, in accordance with various
embodiments.
DETAILED DESCRIPTION
The detailed description of various embodiments herein makes
reference to the accompanying drawings, which show the exemplary
embodiments by way of illustration. While these exemplary
embodiments are described in sufficient detail to enable those
skilled in the art to practice the disclosure, it should be
understood that other embodiments may be realized and that logical
and mechanical changes may be made without departing from the
spirit and scope of the disclosure. Thus, the detailed description
herein is presented for purposes of illustration only and not of
limitation. For example, the steps recited in any of the method or
process descriptions may be executed in any order and are not
limited to the order presented. Moreover, any of the functions or
steps may be outsourced to or performed by one or more third
parties. Furthermore, any reference to singular includes plural
embodiments, and any reference to more than one component may
include a singular embodiment.
With reference to FIG. 1, an exemplary system 100 for data
amelioration and reformation is disclosed, in accordance with
various embodiments. System 100, in operation, may have the
capability to break down a transaction amount into any and all
individual, distinct charges comprised in the transaction amount.
For example, a consumer may rent a car, which has a primary charge
associated with the price of the rental, and additional charges
(e.g., fees or costs) associated with the rental. The transaction
amount is the total of the primary charge plus any additional
charges. Examples of additional charges may be collision damage
coverage, extra driver fee, taxes, etc. System 100 may separate the
transaction amount into individual charge amounts (i.e., the
primary charge and each additional charge), and identify the
individual charge amounts. Therefore, system 100 may be used to
report transactions to parties, such as merchants, that monitor on
what their employees spend company money. For example, employees of
a merchant may not be authorized to buy collision damage coverage
when renting a vehicle for company purposes. System 100 may break
down the rental car transaction amount into individually identified
charges, allowing the employer, merchant, employee, or others to
see what charges were incurred, and which will be reimbursed to the
employee. As another example, system 100 may also aid cost
monitoring in transaction negotiations between parties, in which
the parties may agree that certain individual charges will not be
incurred by a party.
In various embodiments, system 100 may comprise a data ingestion
system 130, a batch processing system 140, a speed processing
system 150, and/or a data servicing system 160. All or any subset
of components of system 100 may be in communication with one
another via a network. System 100 may be computer-based, and may
comprise a processor, a tangible non-transitory computer-readable
memory, and/or a network interface. Instructions stored on the
tangible non-transitory memory may allow system 100 to perform
various functions, as described herein.
In various embodiments, system 100 may receive the data to be
processed by a data source 90. Data source 90 may be any system
associated with an individual or entity which provides data about
the individual and/or entity. For example, data source 90 may be a
merchant's global distribution system, a business travel account,
travel records including airfare, rental vehicles, hotels, etc.,
human resources policies, line item details, accounts receivable,
transaction histories and/or behavior associated with consumers
and/or merchants, purchase policies associated with a consumer
and/or merchant, and/or the like. The data provided by data source
90 may relate to how an individual or entity conducts transactions
and/or business (e.g., what prices they charge/pay, what fees or
costs are associated with certain goods/services, the dates of
transactions, etc.). For example, the data provided by data source
90 may be transaction information for various transactions, wherein
the transaction information for a transaction may comprise a set of
information such as the price, date, product or service, location,
merchant information (e.g., a merchant identifier), and/or the
like. Overall, the data provided by data source 90 may be referred
to as market information, which may include charge information
(e.g., prices of goods and services ("primary charges"), and other
charges associated with primary charges ("ancillary charges")),
purchase and/or transaction histories and trends, etc. In various
embodiments, data source 90 may transmit data to system 100 for
processing.
In various embodiments, data ingestion system 130 may comprise
hardware and/or software capable of storing data and/or analyzing
information. Data ingestion system 130 may comprise a server
appliance running a suitable server operating system (e.g.,
MICROSOFT INTERNET INFORMATION SERVICES or, "IIS") and having
database software (e.g., ORACLE) installed thereon. Data ingestion
system 130 may be in electronic communication with data source 90,
and may receive market information from data source 90. In various
embodiments, the data received from data source 90 by data
ingestion system 130 may be raw data, so data ingestion system 130
may prepare the raw data for further processing by system 100. In
various embodiments data ingestion system 130 may tabulate the raw
data and/or validate the raw data received from data source 90
(e.g., confirm whether the date of a transaction data point is
correct, and/or the like). In various embodiments, data ingestion
system 130 may assign a key to each piece of data received from
data source 90. The key may be a unique number, timestamp, source
identifier, and/or the like, and may be used to identify each
respective piece of data.
In various embodiments, batch processing system 140 may comprise
hardware and/or software capable of storing data and/or analyzing
information. Batch processing system 140 may comprise a server
appliance running a suitable server operating system (e.g.,
MICROSOFT INTERNET INFORMATION SERVICES or, "IIS") and having
database software (e.g., ORACLE) installed thereon. Batch
processing system 140 may be in electronic communication with data
ingestion system 130, and may receive the prepared data from data
ingestion system 130. In various embodiments, batch processing
system 140 may comprise a rules engine 145, which provides the
processing or matching rules and/or parameters for a matching
engine 142 and/or an enrichment engine 147 comprised in batch
processing system 140. Rules engine 145 may instruct matching
engine 142 to identify attributes of the prepared data and match
the prepared data with stored data (e.g., stored transaction
information) having like attributes. Stored data may be transaction
information or data collected by system 100 and/or provided by a
third party. For example, the attributes of prepared data
associated with a plane ticket will be different than the
attributes of prepared data associated with a hotel rental.
Therefore, based on the matching rules from rules engine 145,
matching engine 142 may match data associated with plane tickets
with like plane ticket data from the stored data, and match data
associated with hotel rentals with like hotel rental data from the
stored data. The stored transaction information may be enhanced
and/or enriched by system 100 by matching like stored data based on
processing and matching rules from rules engine 145 and enriching
the stored data.
With reference to FIG. 2A, a matching engine 200 (an example of
matching engine 142 in FIG. 1) is depicted, in accordance with
various embodiments. Matching engine 200 may comprise hardware
and/or software capable of storing data and/or analyzing
information. Matching engine 200 may receive data to match 203 from
data ingestion system 130. Data to match 203 may be from a data
source 90 (e.g., accounts receivable), and may be compared to,
and/or matched with, stored transaction information gathered by
system 100 and/or provided by a third party. Matching engine 200
may calculate the distance and/or difference between data to match
203, or the distance and/or difference between data to match 203
and stored transaction information, via distance calculation engine
206. Data to match 203 may comprise various information (e.g.,
transaction information for various transactions), such as consumer
names, dates of transactions, monetary values, or the like.
Distance calculation engine 206 may comprise settings which
instruct distance calculation engine 206 how to process data to
match 203 in determining the distance/difference between data.
Distance calculation engine 206, for example, may calculate
normalized distance strings, distance numeric, and/or distance
date/time of data to match 203. For example, distance calculation
engine 206 may calculate the distance between two dates, which may
have any date-related unit of measure (e.g., years, days, hours, or
the like) (e.g., the distance between 2016 Feb. 3 and 2016 Feb. 4
is one day). As another example, the difference between numeric
data simply may be a number (e.g., a difference value between
dollar amounts). As another example, string distance may be
measured by any suitable string distance algorithms such as optimal
string alignment, q-gram, cosine similarity, hamming, jaro-winkler,
levenshtein, or the like. The result of a string distance
calculation may be a value between zero and 1, wherein a result of
1 may be a perfect match. For example, the strings "John Doe" and
"Doe Joohn" may have a calculated distance of 0.67. In various
embodiments, any data to match 203 that was separated for
processing (e.g., the date and monetary amount separated from
transaction information) may be rejoined by distance calculation
engine 206 by a key assigned to the data.
In various embodiments, data to match 203 may continue to be
processed in matching engine 200 by being analyzed by a scoring
engine 212 and/or a machine learning engine 214. Scoring engine 212
may assign a matching score to data depending on how close the data
is to matching other data of the same type or a Boolean type data.
The matching score assigned by scoring engine 212 may be a score
between zero and 1, for example, wherein a matching score of 1
between matched data indicates an exact match. A matching score may
be assigned to each piece of data, or to a set of data, such as a
set of transaction information (e.g., price, date, product or
service, location, a merchant identifier, etc.). In various
embodiments, a piece of data may be given a weight, which indicates
its importance in analyzing it in comparison to or combined with
other data. The weight of a piece of data may be the maximum
matching score that piece of data may receive from scoring engine
212 based on its matching. Going along with the previous examples,
a data set may comprise the dates, numeric, and strings discussed
above. The strings having a matching score of 0.67, may have a
weight of 0.2 (the maximum score the string may achieve from
scoring engine 212 is 0.2). Therefore, of the matching score of the
entire data set between zero and 1, the strings having a score of
0.67 may contribute 0.134 to the overall matching score (0.67 of
the 0.2 possible maximum). The matching scores assigned to the
dates and numeric may be summed with the matching score of the
strings to achieve the overall score assigned to the data. In
total, the data set (sum of all scores of individual pieces of data
in the data set) may have a maximum matching score of 1.
Data to match 203 may also be processed through machine learning
engine 214. Machine learning engine 214 may analyze the data and
data quality. If the data is of low quality and/or low matching
scores assigned by scoring engine 212, machine learning engine 214
may detect such quality and/or matching scores. In response,
machine learning engine 214 may adjust the matching rules provided
by rules engine 145 (in FIG. 1) to matching engine 200 in order for
the data analysis and matching process to produce higher quality
matching results. For example, machine learning engine 214 may
detect a change in the transaction behavior of a consumer or
merchant, which is causing the data quality or data matching scores
provided by scoring engine 212 to decrease. Based on the change in
transaction behavior (e.g., consumers using service A rather than
service B, which was previously more common), machine learning
engine 214 may adjust the matching rules provided to matching
engine 200 to reflect such updated transaction habits, and
therefore, matching engine 200 may produce higher quality data and
matching.
With reference to FIGS. 1, 2A, and 2B, system 100 and/or machine
learning engine 214 may comprise a monitoring system 250 configured
to monitor the data quality/matching scores of matched data and
adjust the matching rules provided to matching engine 200 in
response to low data quality/matching scores. Monitoring system 250
may comprise an external data source 252 which may provide external
data such as mobile data, disputed data, or the like (i.e., any
desired field of data). As an example, monitoring system 250 may
retrieve disputed data (i.e., data or information that a customer
has disputed because of an inaccuracy, incompleteness, and/or the
like) from external data source 252 to analyze the data quality of
the disputed data. The data may be passed between data monitoring
results 254 and a monitoring application 260. Monitoring
application 260 may comprise various monitoring rules 262-266
which, if met, may trigger an alert indicating that the matching
rules provided by rules engine 145 may need to be adjusted. For
example, monitoring rules may comprise monitoring rule 262: a
decreasing matching score (i.e., the data being analyzed by
matching engine 200 is not being adequately matched and/or
receiving poor matching scores from scoring engine 212), monitoring
rule 264: a matching score greater than an allowed maximum matching
score (a matching score too high may indicate a problem with the
matching rules), and/or rule monitoring 266: a changing data
quality ("DQ") per field. Monitoring application 260 may comprise
any additional or alternative monitoring rules. Continuing with the
example above, in which monitoring system 250 retrieved disputed
data from external data source 252, the disputed data may be sent
to monitoring application 260 to determine if the disputed data
meets any of monitoring rules 262-266.
In response to the disputed data meeting at least one of the
monitoring rules 262-266 in monitoring application 260 (for
example, disputed data may have a decreasing matching score,
meeting monitoring rule 262), monitoring system 250 may identify
the algorithm of concern 272 (i.e., the matching rule of concern)
and create an alert 274 to an operator advising of the algorithm of
concern 272. In various embodiments, in response to at least one of
monitoring rules 262-266 being met, monitoring system 250 may
automatically retrain machine learning (block 278), which may
comprise adjusting the matching rules provided to matching engine
142 (e.g., matching engine 200) by rules engine 145. To do so, core
data 276 may be extracted (core data 276 may be provided by a data
source 90), labeled to indicate a desired characteristic, and input
into machine learning engine 214. The labeled data may be core data
276 labeled as having the desired characteristic which monitoring
system 250 will retrain machine learning engine 214 to recognize.
Therefore, as machine learning engine 214 receives the labeled
data, machine learning engine is retrained to recognize the
characteristic labeled on the labeled data (i.e., machine learning
engine 214 is told that data A has characteristic B, for example),
with the goal being that machine learning engine 214 will be able
to recognize the same characteristic in data that is not labeled as
having the characteristic.
In response to retraining machine learning (block 278), machine
learning engine 214 may have adjusted the matching rules based on
the retraining. Therefore, matching engine 200 may match data
implementing the adjusted matching rules, and the matched data is
assigned a matching score by scoring engine 212. Monitoring system
250 may determine if the current matching score is greater than
(i.e., improved from) the previous matching score (block 280)
(i.e., the matching score before the retraining). If "no,"
monitoring system 250 may send an alert for manual investigation
282 because automatic retraining of machine learning (block 278)
may not provide adequate adjustment of the matching rules for
matching engine 200. If the answer to block 280 is "yes,"
monitoring system 250 may monitor matching engine 200 by performing
automated tests 284 to make sure matching engine 200 is matching
data and producing matching scores between a desired minimum and
maximum matching score. In response to matching engine 200 failing
automated tests 284, an alert for manual investigation 282 may be
sent to an operator. In response to matching engine 200 passing
automated tests 284, the adjusted matching rules through the
retraining of machine learning engine 214 may be released to
production 288 to analyze/match incoming data in system 100. Data
in monitoring system 250 may be constantly or periodically sent
back as data monitoring results 254 for possible further
monitoring.
In various embodiments, returning to FIG. 2A, data to match 203 may
be filtered through filter system 220. Filter system 220 may be set
to select and keep all results, the top ten (or any desired number)
highest scored results, or any other category of data. The selected
data filtered through filter system 220 may be filtered output 224.
In various embodiments, unmatched data 232 may be filtered via
filter system 220 and sent to the beginning of matching engine 200
to join the next data to match 203, which may be analyzed with the
updated rules adjusted by machine learning engine 214 via
monitoring system 250 (in FIG. 2B). Filter system 220, in response,
may provide matched data 228 as output of matching engine 200.
In various embodiments, with reference to FIG. 1, data received by
batch processing system 140 from data ingestion system 130 may be
processed through enrichment engine 147 in addition to, or instead
of, through matching engine 142. Enrichment engine 147 may enrich
the data received from data ingestion system 130 based on rules
provided by rules engine 145. For example, enrichment engine 147
may correct typographical errors in the data, or simplify the data
to show only characteristics of interest (e.g., transaction type,
date, time, location, and/or the like). In various embodiments,
various data sources 90 may have a quality rating associated with
each of them (i.e., the data from one data source may be of a
higher quality or reliability than another data source). For
example, a first data source may have a lower quality rating than a
second data source. In various embodiments, rules engine 145 may
provide rules to enrichment engine 147 instructing enrichment
engine 147 to use the highest quality data possible. As an example
of how enrichment engine 147 may work, system 100 may receive data
from the first data source, and subsequently receive data from the
second data source. The first data source and second data source
may provide various overlapping data (i.e., data of the same type).
Accordingly, in response to system 100 receiving data from the
second data source, enrichment engine 147 may enrich the data by
replacing data from the first data source that is overlapped by the
second data source, because the second data source provides higher
quality data.
In various embodiments, with reference to FIGS. 1 and 2A, filtered
output 224 and/or matched data 228 from matching engine 200 may be
transmitted to data servicing system 160 as a matched view 162 of
the data processed through batch processing system 140. The
enriched data output from enrichment engine 147 may be transmitted
to data servicing system 160 as an enriched view 164 of the data
processed through batch processing system 140. In various
embodiments, raw data (i.e., data not analyzed or processed by
matching engine 142 and/or enrichment engine 147) may be
transmitted by batch processing system 140 to data servicing system
160 as a raw view 166 of the data.
In various embodiments, with continued reference to FIG. 1, the
data from data ingestion system 130 may be transmitted to speed
processing system 150. Speed processing system 150 may process
and/or analyze the data from data ingestion system 130 in the same
or similar way as the engines provided in batch processing system
140, as discussed herein. However, speed processing system 150 may
analyze and/or process streaming data 152 that may be real time, or
near real time data (i.e., data received within five minutes of the
analysis and processing). Therefore, between batch data processing
runs by batch processing system 140, speed processing system 150
may analyze and process streaming data 152 (e.g., matching and
enriching data in a similar way as completed by matching engine 142
and enrichment engine 147, as discussed herein). Speeding
processing system 150 may transmit the analyzed and processed data
to data servicing system 160 as a stream view 168, which may show
data similar to matched view 162 and enriched view 164 from batch
processing system 140. Stream view 168 may comprise analyzed data
from speed processing system 150 that is available for viewing for
a limited amount of time (e.g., five minutes, or any desired
duration) to show the viewer what current data is showing.
Therefore, stream view 168 is continuously replacing older data
with the most recent data.
In various embodiments, data servicing system 160 may store the
analyzed and processed data from batch processing system 140 and
speed processing system 150 until a user requests to view the data.
A user of system 100 may request a certain type of data output 170
by making a query 172, such as requesting information and data
related to a certain transaction or transaction type. Data output
170 may be any data available in data servicing system 160, so a
user may request matched view 162 and/or enriched view 164, and use
such data. A user may request raw view 166 of the data to act upon
the data as the user desires, and/or the user may request stream
view 168 to view the most recent data produced by system 100. Data
output 170 may be sold to users, published for view, and/or the
like.
In various embodiments, data output 170 of system 100 may be
provided to a business department to analyze and manipulate data
output 170 as seen fit, or to merchants, for example, for
monitoring and processing of corporate transaction instruments for
merchant employees (e.g., making sure employees are not incurring
any unauthorized charges). In various embodiments, with combined
reference to FIGS. 1 and 2A, data output 170 may be provided to
consumers on a device (e.g., a mobile device, tablet, personal
computer, desktop computer, laptop computer, etc.). Such consumers
may be on-boarded into system 100 (i.e., the consumer has a
consumer profile with system 100), such that system 100 may be able
to detect transactions the consumer has made or is making, and see
consumer transaction behavior. For example, system 100 may receive
transaction information for a transaction by a consumer, and detect
and match the transaction information with a transaction type
(e.g., a plane ticket). Matching engine 142 may compare one or more
pieces of data comprised in the transaction information to
transaction type data stored in system 100, as described in
relation to matching engine 200 herein. Matching engine 142 may
identify the transaction type of the transaction information by
matching the data in the transaction information with the
transaction type (from the stored data collected by system 100
and/or received from a third party) that results in the best
matching score (i.e., closest match). System 100 may detect that
the consumer associated with the transaction type will be landing
for the air travel in the afternoon on a certain day, and
therefore, that day, system 100 may send as data output 170 to the
consumer, an offer (e.g., for a taxi cab). System 100 may detect
whether the consumer acted upon the offer (e.g., by using the offer
for the taxi cab), or whether the consumer made use of another
transportation service (e.g., a rideshare service) and ignored the
offer. System 100 may detect what service the consumer used (e.g.,
a taxi or rideshare service) based on the merchant identifier
identifying the merchant (which may identify a taxi or rideshare
service, for example). System 100 may detect the consumer's
response to the offer (e.g., accepting a taxi cab, or using a
different service), and based on the consumer's response, machine
learning engine 214 may adjust the rules for processing and
analyzing data provided by rules engine 145 to matching engine 142
and/or enrichment engine 147. For example, additionally referring
to FIG. 2B, one of the additional monitoring rules in monitoring
application 260 may be a decreasing offer acceptance rate, which
may trigger retraining the machine learning (block 278) to adjust
the matching rules. Such an adjustment may reflect a change in
transaction behavior (e.g., consumers prefer ride share services
rather than taxi cabs) to produce higher quality data and
matching.
With respect to FIG. 3, the process flow depicted is merely an
embodiment of various embodiments, and is not intended to limit the
scope of the disclosure. For example, the steps recited in any of
the method or process descriptions may be executed in any order and
are not limited to the order presented. It will be appreciated that
the description herein makes appropriate references not only to the
steps and consumer interface elements depicted in FIG. 3, but also
to the various system components as described above with reference
to FIGS. 1, 2A, and 2B.
As discussed herein, system 100 may be used to separate a
transaction amount and identify individual charges (e.g., a primary
charge and an ancillary charge(s)) making up the transaction
amount. FIG. 3 depicts an exemplary method 300 for separating a
transaction amount, in accordance with various embodiments. In
various embodiments, with combined reference to FIGS. 1-3, system
100 may collect market information (step 302) from a data source
90. Data source 90 may be any source of market information, such as
a merchant's global distribution system, a business travel account,
travel records including airfare, rental vehicles, hotels, etc.,
human resources policies, line item details, accounts receivable,
transaction histories and/or behavior associated with consumers
and/or merchants, purchase policies associated with a consumer
and/or merchant, and/or the like. As discussed herein, system 100
may match and/or enrich the market information (step 304) in any
suitable manner, such as those described herein in association with
matching engine 142 (e.g., matching engine 200) and/or enrichment
engine 147, to produce enhanced market information. Matching engine
142, which receives matching rules from rules engine 145, may match
data by detecting common attributes between data. In various
embodiments, as described herein, matching engine 200 may calculate
the distance and/or difference between data via distance
calculation engine 206, and then score the matched data via scoring
engine 212. System 100 may keep any data desired by filtering the
data via filter system 220.
In various embodiments, system 100 may receive transaction
information for a transaction (step 306). The transaction
information may be provided by a data source 90, or from an account
for a consumer and/or merchant set up with system 100. The
transaction may comprise a transaction amount, which the user of
system 100 and/or data output 170 may desire to separate into
individual charges (e.g., a primary charge(s) and an ancillary
charge(s)) and identify the individual charges. The transaction
information may further comprise the date and time of the
transaction, merchant information, consumer information, payment
information, the goods or services purchased, and/or the like. The
transaction information may be matched with a transaction type
(step 308) associated with the transaction information by matching
engine 200. The transaction information may be matched with the
transaction type by comparing the transaction information to the
matched and/or enhanced market information produced in step 304,
and identifying a transaction type of the market information that
matches the transaction information (e.g., the transaction
information and the relevant market information may comprise the
same or similar merchant information, transaction amount,
good/service type, and/or the like). Matching engine 200 may
compare the transaction information to the enhanced market
information by calculating the difference and/or distance between
the two, and identifying the transaction type based on the closest
match between the transaction information and the matched market
information, as described above in relation to matching engine
200.
In response to matching the transaction information with a
transaction type, in various embodiments, system 100 may retrieve a
possible charge type(s) associated with the transaction type (step
310). The possible charge types may be part of the market
information that is associated with the transaction type. The
possible charge types may be those types offered or included in the
transaction type (e.g., insurance for a rental car). In various
embodiments, the possible charge types may be associated with a
consumer or a merchant, meaning that based on transaction behavior
of the consumer or merchant, system 100 may be able to predict that
the consumer and/or merchant will likely incur or charge the
possible charge types during a transaction. Possible charge types
associated with a transaction type may be the primary charge, and
then any additional fees, upgrades, taxes, or additions associated
with the primary charge (i.e., ancillary charges) associated with a
transaction.
In various embodiments, system 100 may compare the transaction
information with the possible charge types (step 312) associated
with the transaction type. Matching engine 200 may, as described
herein, calculate the distance and/or difference between the
transaction information and the possible charge types to match
parts of the transaction information with one or more possible
charge types. For example, system 100 may be looking for the
good/service purchased and an associated primary charge, merchant
information to identify what types of ancillary charges may be
present, the transaction behavior of the consumer associated with
the transaction information, the date/time of the transaction to
determine what amounts may be associated with which charges, and/or
the like. Matching engine 200 may match one or more possible charge
types with the transaction amount based on the comparison between
the transaction information and the possible charge types. Based on
the comparison, system 100 may identify individual charges making
up the transaction amount (step 314). For example, by knowing the
transaction type is a car rental, and the possible charge types are
the rental price, taxes, insurance, damage payment, and/or the
like, system 100 may be able to determine, based on the transaction
amount and the amounts of the possible charge types, which possible
charge types make up the transaction amount. In other words,
matching engine 200 may calculate which combination of possible
charge types is most similar to the transaction information
including the transaction amount (i.e., having the best matching
score). In response, system 100 may separate the transaction amount
into the identified individual charges (step 316) so that a user of
data output 170 may view each individual charge. Each individual
charge may be a primary charge(s) and/or an ancillary
charge(s).
The various components in system 100 may be independently,
separately or collectively suitably coupled to each other, and/or
network, via data links which include, for example, a connection to
an Internet Service Provider (ISP) over the local loop as is
typically used in connection with standard modem communication,
cable modem, Dish Networks.RTM., ISDN, Digital Subscriber Line
(DSL), or various wireless communication methods, see, e.g.,
GILBERT HELD, UNDERSTANDING DATA COMMUNICATIONS (1996), which is
hereby incorporated by reference. It is noted that the network may
be implemented as other types of networks, such as an interactive
television (ITV) network. Moreover, the system contemplates the
use, sale or distribution of any goods, services or information
over any network having similar functionality described herein.
The disclosure and claims do not describe only a particular outcome
of system 100, but the disclosure and claims include specific rules
for implementing the outcome of system 100 and that render
information into a specific format that is then used and applied to
create the desired results of separating transaction amounts into
identified individual charge amounts, as set forth in McRO, Inc. v.
Bandai Namco Games America Inc. (Fed. Cir. case number 15-1080,
Sep. 13, 2016). In other words, the outcome of system 100 can be
performed by many different types of rules and combinations of
rules, and this disclosure includes various embodiments with
specific rules. While the absence of complete preemption may not
guarantee that a claim is eligible, the disclosure does not
sufficiently preempt the field of data processing at all. The
disclosure acts to narrow, confine, and otherwise tie down the
disclosure so as not to cover the general abstract idea of just
data processing. Significantly, other systems and methods exist for
separating transaction amounts into identified individual charge
amounts, so it would be inappropriate to assert that the claimed
invention preempts the field or monopolizes the basic tools for the
same. In other words, the disclosure will not prevent others from
breaking down charges into sub-charges, because other systems are
already performing the functionality in different ways than the
claimed invention. Moreover, the claimed invention includes an
inventive concept that may be found in the non-conventional and
non-generic arrangement of known, conventional pieces, in
conformance with Bascom v. AT&T Mobility, 2015-1763 (Fed. Cir.
2016). The disclosure and claims go way beyond any conventionality
of any one of the systems in that the interaction and synergy of
the systems leads to additional functionality that is not provided
by any one of the systems operating independently. The disclosure
and claims may also include the interaction between multiple
different systems, so the disclosure cannot be considered an
implementation of a generic computer, or just "apply it" to an
abstract process. The disclosure and claims may also be directed to
improvements to software with a specific implementation of a
solution to a problem in the software arts.
In various embodiments, the system and method may include alerting
a subscriber when their computer is offline. The system may include
generating customized information and alerting a remote subscriber
that the information can be accessed from their computer. The
alerts are generated by filtering received information, building
information alerts and formatting the alerts into data blocks based
upon subscriber preference information. The data blocks are
transmitted to the subscriber's wireless device which, when
connected to the computer, causes the computer to auto-launch an
application to display the information alert and provide access to
more detailed information about the information alert. More
particularly, the method may comprise providing a viewer
application to a subscriber for installation on the remote
subscriber computer; receiving information at a transmission server
sent from a data source over the Internet, the transmission server
comprising a microprocessor and a memory that stores the remote
subscriber's preferences for information format, destination
address, specified information, and transmission schedule, wherein
the microprocessor filters the received information by comparing
the received information to the specified information; generates an
information alert from the filtered information that contains a
name, a price and a universal resource locator (URL), which
specifies the location of the data source; formats the information
alert into data blocks according to said information format; and
transmits the formatted information alert over a wireless
communication channel to a wireless device associated with a
subscriber based upon the destination address and transmission
schedule, wherein the alert activates the application to cause the
information alert to display on the remote subscriber computer and
to enable connection via the URL to the data source over the
Internet when the wireless device is locally connected to the
remote subscriber computer and the remote subscriber computer comes
online.
In various embodiments, the system and method may include a
graphical user interface for dynamically relocating/rescaling
obscured textual information (e.g., in the display of data output
170) of an underlying window to become automatically viewable to
the user. By permitting textual information to be dynamically
relocated based on an overlap condition, the computer's ability to
display information is improved. More particularly, the method for
dynamically relocating textual information within an underlying
window displayed in a graphical user interface may comprise
displaying a first window containing textual information in a first
format within a graphical user interface on a computer screen;
displaying a second window within the graphical user interface;
constantly monitoring the boundaries of the first window and the
second window to detect an overlap condition where the second
window overlaps the first window such that the textual information
in the first window is obscured from a user's view; determining the
textual information would not be completely viewable if relocated
to an unobstructed portion of the first window; calculating a first
measure of the area of the first window and a second measure of the
area of the unobstructed portion of the first window; calculating a
scaling factor which is proportional to the difference between the
first measure and the second measure; scaling the textual
information based upon the scaling factor; automatically relocating
the scaled textual information, by a processor, to the unobscured
portion of the first window in a second format during an overlap
condition so that the entire scaled textual information is viewable
on the computer screen by the user; and automatically returning the
relocated scaled textual information, by the processor, to the
first format within the first window when the overlap condition no
longer exists.
In various embodiments, the system may also include isolating and
removing malicious code from electronic messages (e.g., email) to
prevent a computer from being compromised, for example by being
infected with a computer virus. The system may scan electronic
communications for malicious computer code and clean the electronic
communication before it may initiate malicious acts. The system
operates by physically isolating a received electronic
communication in a "quarantine" sector of the computer memory. A
quarantine sector is a memory sector created by the computer's
operating system such that files stored in that sector are not
permitted to act on files outside that sector. When a communication
containing malicious code is stored in the quarantine sector, the
data contained within the communication is compared to malicious
code-indicative patterns stored within a signature database. The
presence of a particular malicious code-indicative pattern
indicates the nature of the malicious code. The signature database
further includes code markers that represent the beginning and end
points of the malicious code. The malicious code is then extracted
from malicious code-containing communication. An extraction routine
is run by a file parsing component of the processing unit. The file
parsing routine performs the following operations: scan the
communication for the identified beginning malicious code marker;
flag each scanned byte between the beginning marker and the
successive end malicious code marker; continue scanning until no
further beginning malicious code marker is found; and create a new
data file by sequentially copying all non-flagged data bytes into
the new file, which thus forms a sanitized communication file. The
new, sanitized communication is transferred to a non-quarantine
sector of the computer memory. Subsequently, all data on the
quarantine sector is erased. More particularly, the system includes
a method for protecting a computer from an electronic communication
containing malicious code by receiving an electronic communication
containing malicious code in a computer with a memory having a boot
sector, a quarantine sector and a non-quarantine sector; storing
the communication in the quarantine sector of the memory of the
computer, wherein the quarantine sector is isolated from the boot
and the non-quarantine sector in the computer memory, where code in
the quarantine sector is prevented from performing write actions on
other memory sectors; extracting, via file parsing, the malicious
code from the electronic communication to create a sanitized
electronic communication, wherein the extracting comprises scanning
the communication for an identified beginning malicious code
marker, flagging each scanned byte between the beginning marker and
a successive end malicious code marker, continuing scanning until
no further beginning malicious code marker is found, and creating a
new data file by sequentially copying all non-flagged data bytes
into a new file that forms a sanitized communication file;
transferring the sanitized electronic communication to the
non-quarantine sector of the memory; and deleting all data
remaining in the quarantine sector.
In various embodiments, the system may also address the problem of
retaining control over customers during affiliate purchase
transactions, using a system for co-marketing the "look and feel"
of the host web page with the product-related content information
of the advertising merchant's web page. The system can be operated
by a third-party outsource provider, who acts as a broker between
multiple hosts and merchants. Prior to implementation, a host
places links to a merchant's webpage on the host's web page. The
links are associated with product-related content on the merchant's
web page. Additionally, the outsource provider system stores the
"look and feel" information from each host's web pages in a
computer data store, which is coupled to a computer server. The
"look and feel" information includes visually perceptible elements
such as logos, colors, page layout, navigation system, frames,
mouse-over effects or other elements that are consistent through
some or all of each host's respective web pages. A customer who
clicks on an advertising link is not transported from the host web
page to the merchant's web page, but instead is re-directed to a
composite web page that combines product information associated
with the selected item and visually perceptible elements of the
host web page. The outsource provider's server responds by first
identifying the host web page where the link has been selected and
retrieving the corresponding stored "look and feel" information.
The server constructs a composite web page using the retrieved
"look and feel" information of the host web page, with the
product-related content embedded within it, so that the composite
web page is visually perceived by the customer as associated with
the host web page. The server then transmits and presents this
composite web page to the customer so that she effectively remains
on the host web page to purchase the item without being redirected
to the third party merchant affiliate. Because such composite pages
are visually perceived by the customer as associated with the host
web page, they give the customer the impression that she is viewing
pages served by the host. Further, the customer is able to purchase
the item without being redirected to the third party merchant
affiliate, thus allowing the host to retain control over the
customer. This system enables the host to receive the same
advertising revenue streams as before but without the loss of
visitor traffic and potential customers. More particularly, the
system may be useful in an outsource provider serving web pages
offering commercial opportunities. The computer store containing
data, for each of a plurality of first web pages, defining a
plurality of visually perceptible elements, which visually
perceptible elements correspond to the plurality of first web
pages; wherein each of the first web pages belongs to one of a
plurality of web page owners; wherein each of the first web pages
displays at least one active link associated with a commerce object
associated with a buying opportunity of a selected one of a
plurality of merchants; and wherein the selected merchant, the
outsource provider, and the owner of the first web page displaying
the associated link are each third parties with respect to one
other; a computer server at the outsource provider, which computer
server is coupled to the computer store and programmed to: receive
from the web browser of a computer user a signal indicating
activation of one of the links displayed by one of the first web
pages; automatically identify as the source page the one of the
first web pages on which the link has been activated; in response
to identification of the source page, automatically retrieve the
stored data corresponding to the source page; and using the data
retrieved, automatically generate and transmit to the web browser a
second web page that displays: information associated with the
commerce object associated with the link that has been activated,
and the plurality of visually perceptible elements visually
corresponding to the source page.
Systems, methods and computer program products are provided. In the
detailed description herein, references to "various embodiments",
"one embodiment", "an embodiment", "an example embodiment", etc.,
indicate that the embodiment described may include a particular
feature, structure, or characteristic, but every embodiment may not
necessarily include the particular feature, structure, or
characteristic. Moreover, such phrases are not necessarily
referring to the same embodiment. Further, when a particular
feature, structure, or characteristic is described in connection
with an embodiment, it is submitted that it is within the knowledge
of one skilled in the art to affect such feature, structure, or
characteristic in connection with other embodiments whether or not
explicitly described. After reading the description, it will be
apparent to one skilled in the relevant art(s) how to implement the
disclosure in alternative embodiments.
As used herein, "satisfy", "meet", "match", "associated with" or
similar phrases may include an identical match, a partial match,
meeting certain criteria, matching a subset of data, a correlation,
satisfying certain criteria, a correspondence, an association, an
algorithmic relationship and/or the like. Similarly, as used
herein, "authenticate" or similar terms may include an exact
authentication, a partial authentication, authenticating a subset
of data, a correspondence, satisfying certain criteria, an
association, an algorithmic relationship and/or the like.
Terms and phrases similar to "associate" and/or "associating" may
include tagging, flagging, correlating, using a look-up table or
any other method or system for indicating or creating a
relationship between elements, such as, for example, (i) a
transaction account and (ii) an item (e.g., offer, reward,
discount) and/or digital channel. Moreover, the associating may
occur at any point, in response to any suitable action, event, or
period of time. The associating may occur at pre-determined
intervals, periodic, randomly, once, more than once, or in response
to a suitable request or action. Any of the information may be
distributed and/or accessed via a software enabled link, wherein
the link may be sent via an email, text, post, social network input
and/or any other method known in the art.
The customer may be identified as a customer of interest to a
merchant based on the customer's transaction history at the
merchant, types of transactions, type of transaction account,
frequency of transactions, number of transactions, lack of
transactions, timing of transactions, transaction history at other
merchants, demographic information, personal information (e.g.,
gender, race, religion), social media or any other online
information, potential for transacting with the merchant and/or any
other factors.
The phrases consumer, customer, user, account holder, account
affiliate, cardmember or the like shall include any person, entity,
business, government organization, business, software, hardware,
machine associated with a transaction account, buys merchant
offerings offered by one or more merchants using the account and/or
who is legally designated for performing transactions on the
account, regardless of whether a physical card is associated with
the account. For example, the cardmember may include a transaction
account owner, a transaction account user, an account affiliate, a
child account user, a subsidiary account user, a beneficiary of an
account, a custodian of an account, and/or any other person or
entity affiliated or associated with a transaction account.
As used herein, big data may refer to partially or fully
structured, semi-structured, or unstructured data sets including
millions of rows and hundreds of thousands of columns. A big data
set may be compiled, for example, from a history of purchase
transactions over time, from web registrations, from social media,
from records of charge (ROC), from summaries of charges (SOC), from
internal data, or from other suitable sources. Big data sets may be
compiled without descriptive metadata such as column types, counts,
percentiles, or other interpretive-aid data points.
A record of charge (or "ROC") may comprise any transaction or
transaction data. The ROC may be a unique identifier associated
with a transaction. Record of Charge (ROC) data includes important
information and enhanced data. For example, a ROC may contain
details such as location, merchant name or identifier, transaction
amount, transaction date, account number, account security pin or
code, account expiry date, and the like for the transaction. Such
enhanced data increases the accuracy of matching the transaction
data to the receipt data. Such enhanced ROC data is NOT equivalent
to transaction entries from a banking statement or transaction
account statement, which is very limited to basic data about a
transaction. Furthermore, a ROC is provided by a different source,
namely the ROC is provided by the merchant to the transaction
processor. In that regard, the ROC is a unique identifier
associated with a particular transaction. A ROC is often associated
with a Summary of Charges (SOC). The ROCs and SOCs include
information provided by the merchant to the transaction processor,
and the ROCs and SOCs are used in the settlement process with the
merchant. A transaction may, in various embodiments, be performed
by a one or more members using a transaction account, such as a
transaction account associated with a gift card, a debit card, a
credit card, and the like.
Distributed computing cluster may be, for example, a Hadoop.RTM.
cluster configured to process and store big data sets with some of
nodes comprising a distributed storage system and some of nodes
comprising a distributed processing system. In that regard,
distributed computing cluster may be configured to support a
Hadoop.RTM. distributed file system (HDFS) as specified by the
Apache Software Foundation at http://hadoop.apache.org/docs/. For
more information on big data management systems, see U.S. Ser. No.
14/944,902 titled INTEGRATED BIG DATA INTERFACE FOR MULTIPLE
STORAGE TYPES and filed on Nov. 18, 2015; U.S. Ser. No. 14/944,979
titled SYSTEM AND METHOD FOR READING AND WRITING TO BIG DATA
STORAGE FORMATS and filed on Nov. 18, 2015; U.S. Ser. No.
14/945,032 titled SYSTEM AND METHOD FOR CREATING, TRACKING, AND
MAINTAINING BIG DATA USE CASES and filed on Nov. 18, 2015; U.S.
Ser. No. 14/944,849 titled SYSTEM AND METHOD FOR AUTOMATICALLY
CAPTURING AND RECORDING LINEAGE DATA FOR BIG DATA RECORDS and filed
on Nov. 18, 2015; U.S. Ser. No. 14/944,898 titled SYSTEMS AND
METHODS FOR TRACKING SENSITIVE DATA IN A BIG DATA ENVIRONMENT and
filed on Nov. 18, 2015; and U.S. Ser. No. 14/944,961 titled SYSTEM
AND METHOD TRANSFORMING SOURCE DATA INTO OUTPUT DATA IN BIG DATA
ENVIRONMENTS and filed on Nov. 18, 2015, the contents of each of
which are herein incorporated by reference in their entirety.
Any communication, transmission and/or channel discussed herein may
include any system or method for delivering content (e.g. data,
information, metadata, etc), and/or the content itself. The content
may be presented in any form or medium, and in various embodiments,
the content may be delivered electronically and/or capable of being
presented electronically. For example, a channel may comprise a
website or device (e.g., Facebook, YOUTUBE.RTM., APPLE.RTM.TV.RTM.,
PANDORA.RTM., XBOX.RTM., SONY.RTM. PLAYSTATION.RTM.), a uniform
resource locator ("URL"), a document (e.g., a MICROSOFT.RTM.
Word.RTM. document, a MICROSOFT.RTM. Excel.RTM. document, an
ADOBE.RTM. .pdf document, etc.), an "ebook," an "emagazine," an
application or microapplication (as described herein), an SMS or
other type of text message, an email, facebook, twitter, MMS and/or
other type of communication technology. In various embodiments, a
channel may be hosted or provided by a data partner. In various
embodiments, the distribution channel may comprise at least one of
a merchant website, a social media website, affiliate or partner
websites, an external vendor, a mobile device communication, social
media network and/or location based service. Distribution channels
may include at least one of a merchant website, a social media
site, affiliate or partner websites, an external vendor, and a
mobile device communication. Examples of social media sites include
FACEBOOK.RTM., FOURSQUARE.RTM., TWITTER.RTM., MYSPACE.RTM.,
LINKEDIN.RTM., and the like. Examples of affiliate or partner
websites include AMERICAN EXPRESS.RTM., GROUPON.RTM.,
LIVINGSOCIAL.RTM., and the like. Moreover, examples of mobile
device communications include texting, email, and mobile
applications for smartphones.
A "consumer profile" or "consumer profile data" may comprise any
information or data about a consumer that describes an attribute
associated with the consumer (e.g., a preference, an interest,
demographic information, personally identifying information, and
the like).
In various embodiments, the methods described herein are
implemented using the various particular machines described herein.
The methods described herein may be implemented using the below
particular machines, and those hereinafter developed, in any
suitable combination, as would be appreciated immediately by one
skilled in the art. Further, as is unambiguous from this
disclosure, the methods described herein may result in various
transformations of certain articles.
For the sake of brevity, conventional data networking, application
development and other functional aspects of the systems (and
components of the individual operating components of the systems)
may not be described in detail herein. Furthermore, the connecting
lines shown in the various figures contained herein are intended to
represent exemplary functional relationships and/or physical
couplings between the various elements. It should be noted that
many alternative or additional functional relationships or physical
connections may be present in a practical system.
The various system components discussed herein may include one or
more of the following: a host server or other computing systems
including a processor for processing digital data; a memory coupled
to the processor for storing digital data; an input digitizer
coupled to the processor for inputting digital data; an application
program stored in the memory and accessible by the processor for
directing processing of digital data by the processor; a display
device coupled to the processor and memory for displaying
information derived from digital data processed by the processor;
and a plurality of databases. Various databases used herein may
include: client data; merchant data; financial institution data;
and/or like data useful in the operation of the system. As those
skilled in the art will appreciate, user computer may include an
operating system (e.g., WINDOWS.RTM., OS2, UNIX.RTM., LINUX.RTM.,
SOLARIS.RTM., MacOS, etc.) as well as various conventional support
software and drivers typically associated with computers.
The present system or any part(s) or function(s) thereof may be
implemented using hardware, software or a combination thereof and
may be implemented in one or more computer systems or other
processing systems. However, the manipulations performed by
embodiments were often referred to in terms, such as matching or
selecting, which are commonly associated with mental operations
performed by a human operator. No such capability of a human
operator is necessary, or desirable in most cases, in any of the
operations described herein. Rather, the operations may be machine
operations. Useful machines for performing the various embodiments
include general purpose digital computers or similar devices.
In fact, in various embodiments, the embodiments are directed
toward one or more computer systems capable of carrying out the
functionality described herein. The computer system includes one or
more processors, such as processor. The processor is connected to a
communication infrastructure (e.g., a communications bus,
cross-over bar, or network). Various software embodiments are
described in terms of this exemplary computer system. After reading
this description, it will become apparent to a person skilled in
the relevant art(s) how to implement various embodiments using
other computer systems and/or architectures. Computer system can
include a display interface that forwards graphics, text, and other
data from the communication infrastructure (or from a frame buffer
not shown) for display on a display unit.
Computer system also includes a main memory, such as for example
random access memory (RAM), and may also include a secondary
memory. The secondary memory may include, for example, a hard disk
drive and/or a removable storage drive, representing a floppy disk
drive, a magnetic tape drive, an optical disk drive, etc. The
removable storage drive reads from and/or writes to a removable
storage unit in a well-known manner. Removable storage unit
represents a floppy disk, magnetic tape, optical disk, etc. which
is read by and written to by removable storage drive. As will be
appreciated, the removable storage unit includes a computer usable
storage medium having stored therein computer software and/or
data.
In various embodiments, secondary memory may include other similar
devices for allowing computer programs or other instructions to be
loaded into computer system. Such devices may include, for example,
a removable storage unit and an interface. Examples of such may
include a program cartridge and cartridge interface (such as that
found in video game devices), a removable memory chip (such as an
erasable programmable read only memory (EPROM), or programmable
read only memory (PROM)) and associated socket, and other removable
storage units and interfaces, which allow software and data to be
transferred from the removable storage unit to computer system.
Computer system may also include a communications interface.
Communications interface allows software and data to be transferred
between computer system and external devices. Examples of
communications interface may include a modem, a network interface
(such as an Ethernet card), a communications port, a Personal
Computer Memory Card International Association (PCMCIA) slot and
card, etc. Software and data transferred via communications
interface are in the form of signals which may be electronic,
electromagnetic, optical or other signals capable of being received
by communications interface. These signals are provided to
communications interface via a communications path (e.g., channel).
This channel carries signals and may be implemented using wire,
cable, fiber optics, a telephone line, a cellular link, a radio
frequency (RF) link, wireless and other communications
channels.
The terms "computer program medium" and "computer usable medium"
and "computer readable medium" are used to generally refer to media
such as removable storage drive and a hard disk installed in hard
disk drive. These computer program products provide software to
computer system.
Computer programs (also referred to as computer control logic) are
stored in main memory and/or secondary memory. Computer programs
may also be received via communications interface. Such computer
programs, when executed, enable the computer system to perform the
features as discussed herein. In particular, the computer programs,
when executed, enable the processor to perform the features of
various embodiments. Accordingly, such computer programs represent
controllers of the computer system.
In various embodiments, software may be stored in a computer
program product and loaded into computer system using removable
storage drive, hard disk drive or communications interface. The
control logic (software), when executed by the processor, causes
the processor to perform the functions of various embodiments as
described herein. In various embodiments, hardware components such
as application specific integrated circuits (ASICs). Implementation
of the hardware state machine so as to perform the functions
described herein will be apparent to persons skilled in the
relevant art(s).
In various embodiments, the server may include application servers
(e.g. WEB SPHERE, WEB LOGIC, JBOSS, EDB.RTM. Postgres Plus Advanced
Server.RTM. (PPAS), etc.). In various embodiments, the server may
include web servers (e.g. APACHE, IIS, GWS, SUN JAVA.RTM. SYSTEM
WEB SERVER).
A web client includes any device (e.g., personal computer) which
communicates via any network, for example such as those discussed
herein. Such browser applications comprise Internet browsing
software installed within a computing unit or a system to conduct
online transactions and/or communications. These computing units or
systems may take the form of a computer or set of computers,
although other types of computing units or systems may be used,
including laptops, notebooks, tablets, hand held computers,
personal digital assistants, set-top boxes, workstations,
computer-servers, main frame computers, mini-computers, PC servers,
pervasive computers, network sets of computers, personal computers,
such as IPADS.RTM., IMACS.RTM., and MACBOOKS.RTM., kiosks,
terminals, point of sale (POS) devices and/or terminals,
televisions, or any other device capable of receiving data over a
network. A web-client may run MICROSOFT.RTM. INTERNET
EXPLORER.RTM., MOZILLA.RTM. FIREFOX.RTM., GOOGLE.RTM. CHROME.RTM.,
APPLE.RTM. Safari, or any other of the myriad software packages
available for browsing the interne.
As those skilled in the art will appreciate, a web client includes
an operating system (e.g., WINDOWS.RTM./CE/Mobile, OS2, UNIX.RTM.,
LINUX.RTM., SOLARIS.RTM., MacOS, etc.) as well as various
conventional support software and drivers typically associated with
computers. A web client may include any suitable personal computer,
network computer, workstation, personal digital assistant, cellular
phone, smart phone, minicomputer, mainframe or the like. A web
client can be in a home or business environment with access to a
network. In various embodiments, access is through a network or the
Internet through a commercially available web-browser software
package. A web client may implement security protocols such as
Secure Sockets Layer (SSL) and Transport Layer Security (TLS). A
web client may implement several application layer protocols
including http, https, ftp, and sftp.
In various embodiments, components, modules, and/or engines of
system 100 may be implemented as micro-applications or micro-apps.
Micro-apps are typically deployed in the context of a mobile
operating system, including for example, a WINDOWS.RTM. mobile
operating system, an ANDROID.RTM. Operating System, APPLE.RTM.
IOS.RTM., a BLACKBERRY.RTM. operating system and the like. The
micro-app may be configured to leverage the resources of the larger
operating system and associated hardware via a set of predetermined
rules which govern the operations of various operating systems and
hardware resources. For example, where a micro-app desires to
communicate with a device or network other than the mobile device
or mobile operating system, the micro-app may leverage the
communication protocol of the operating system and associated
device hardware under the predetermined rules of the mobile
operating system. Moreover, where the micro-app desires an input
from a user, the micro-app may be configured to request a response
from the operating system which monitors various hardware
components and then communicates a detected input from the hardware
to the micro-app.
As used herein an "identifier" may be any suitable identifier that
uniquely identifies an item. For example, the identifier may be a
globally unique identifier ("GUID"). The GUID may be an identifier
created and/or implemented under the universally unique identifier
standard. Moreover, the GUID may be stored as 128-bit value that
can be displayed as 32 hexadecimal digits. The identifier may also
include a major number, and a minor number. The major number and
minor number may each be 16 bit integers.
As used herein, the term "network" includes any cloud, cloud
computing system or electronic communications system or method
which incorporates hardware and/or software components.
Communication among the parties may be accomplished through any
suitable communication channels, such as, for example, a telephone
network, an extranet, an intranet, Internet, point of interaction
device (point of sale device, personal digital assistant (e.g.,
IPHONE.RTM., BLACKBERRY.RTM.), cellular phone, kiosk, etc.), online
communications, satellite communications, off-line communications,
wireless communications, transponder communications, local area
network (LAN), wide area network (WAN), virtual private network
(VPN), networked or linked devices, keyboard, mouse and/or any
suitable communication or data input modality. Moreover, although
the system is frequently described herein as being implemented with
TCP/IP communications protocols, the system may also be implemented
using IPX, APPLE.RTM. talk, IP-6, NetBIOS.RTM., OSI, any tunneling
protocol (e.g. IPsec, SSH), or any number of existing or future
protocols. If the network is in the nature of a public network,
such as the Internet, it may be advantageous to presume the network
to be insecure and open to eavesdroppers. Specific information
related to the protocols, standards, and application software
utilized in connection with the Internet is generally known to
those skilled in the art and, as such, need not be detailed herein.
See, for example, DILIP NAIK, INTERNET STANDARDS AND PROTOCOLS
(1998); JAVA.RTM. 2 COMPLETE, various authors, (Sybex 1999);
DEBORAH RAY AND ERIC RAY, MASTERING HTML 4.0 (1997); and LOSHIN,
TCP/IP CLEARLY EXPLAINED (1997) and DAVID GOURLEY AND BRIAN TOTTY,
HTTP, THE DEFINITIVE GUIDE (2002), the contents of which are hereby
incorporated by reference.
"Cloud" or "Cloud computing" includes a model for enabling
convenient, on-demand network access to a shared pool of
configurable computing resources (e.g., networks, servers, storage,
applications, and services) that can be rapidly provisioned and
released with minimal management effort or service provider
interaction. Cloud computing may include location-independent
computing, whereby shared servers provide resources, software, and
data to computers and other devices on demand. For more information
regarding cloud computing, see the NIST's (National Institute of
Standards and Technology) definition of cloud computing at
http://csrc.nist.gov/publications/nistpubs/800-145/SP800-145.pdf
(last visited June 2012), which is hereby incorporated by reference
in its entirety.
As used herein, "transmit" may include sending electronic data from
one system component to another over a network connection.
Additionally, as used herein, "data" may include encompassing
information such as commands, queries, files, data for storage, and
the like in digital or any other form.
Phrases and terms similar to an "item" may include any good,
service, information, experience, entertainment, data, offer,
discount, rebate, points, virtual currency, content, access,
rental, lease, contribution, account, credit, debit, benefit,
right, reward, points, coupons, credits, monetary equivalent,
anything of value, something of minimal or no value, monetary
value, non-monetary value and/or the like. Moreover, the
"transactions" or "purchases" discussed herein may be associated
with an item. Furthermore, a "reward" may be an item.
The system contemplates uses in association with web services,
utility computing, pervasive and individualized computing, security
and identity solutions, autonomic computing, cloud computing,
commodity computing, mobility and wireless solutions, open source,
biometrics, grid computing and/or mesh computing.
Any databases discussed herein may include relational,
hierarchical, graphical, blockchain, object-oriented structure
and/or any other database configurations. Common database products
that may be used to implement the databases include DB2 by IBM.RTM.
(Armonk, N.Y.), various database products available from
ORACLE.RTM. Corporation (Redwood Shores, Calif.), MICROSOFT.RTM.
Access.RTM. or MICROSOFT.RTM. SQL Server.RTM. by MICROSOFT.RTM.
Corporation (Redmond, Wash.), MySQL by MySQL AB (Uppsala, Sweden),
MongoDB.RTM., Redis.RTM., Apache Cassandra.RTM., or any other
suitable database product. Moreover, the databases may be organized
in any suitable manner, for example, as data tables or lookup
tables. Each record may be a single file, a series of files, a
linked series of data fields or any other data structure.
The blockchain structure may include a distributed database that
maintains a growing list of data records. The blockchain may
provide enhanced security because each block may hold individual
transactions and the results of any blockchain executables. Each
block may contain a timestamp and a link to a previous block.
Blocks may be linked because each block may include the hash of the
prior block in the blockchain. The linked blocks form a chain, with
only one successor block allowed to link to one other predecessor
block.
Association of certain data may be accomplished through any desired
data association technique such as those known or practiced in the
art. For example, the association may be accomplished either
manually or automatically. Automatic association techniques may
include, for example, a database search, a database merge, GREP,
AGREP, SQL, using a key field in the tables to speed searches,
sequential searches through all the tables and files, sorting
records in the file according to a known order to simplify lookup,
and/or the like. The association step may be accomplished by a
database merge function, for example, using a "key field" in
pre-selected databases or data sectors. Various database tuning
steps are contemplated to optimize database performance. For
example, frequently used files such as indexes may be placed on
separate file systems to reduce In/Out ("I/O") bottlenecks.
More particularly, a "key field" partitions the database according
to the high-level class of objects defined by the key field. For
example, certain types of data may be designated as a key field in
a plurality of related data tables and the data tables may then be
linked on the basis of the type of data in the key field. The data
corresponding to the key field in each of the linked data tables is
preferably the same or of the same type. However, data tables
having similar, though not identical, data in the key fields may
also be linked by using AGREP, for example. In accordance with one
embodiment, any suitable data storage technique may be utilized to
store data without a standard format. Data sets may be stored using
any suitable technique, including, for example, storing individual
files using an ISO/IEC 7816-4 file structure; implementing a domain
whereby a dedicated file is selected that exposes one or more
elementary files containing one or more data sets; using data sets
stored in individual files using a hierarchical filing system; data
sets stored as records in a single file (including compression, SQL
accessible, hashed via one or more keys, numeric, alphabetical by
first tuple, etc.); Binary Large Object (BLOB); stored as ungrouped
data elements encoded using ISO/IEC 7816-6 data elements; stored as
ungrouped data elements encoded using ISO/IEC Abstract Syntax
Notation (ASN.1) as in ISO/IEC 8824 and 8825; and/or other
proprietary techniques that may include fractal compression
methods, image compression methods, etc.
In various embodiments, the ability to store a wide variety of
information in different formats is facilitated by storing the
information as a BLOB. Thus, any binary information can be stored
in a storage space associated with a data set. As discussed above,
the binary information may be stored in association with the system
or external to but affiliated with system. The BLOB method may
store data sets as ungrouped data elements formatted as a block of
binary via a fixed memory offset using either fixed storage
allocation, circular queue techniques, or best practices with
respect to memory management (e.g., paged memory, least recently
used, etc.). By using BLOB methods, the ability to store various
data sets that have different formats facilitates the storage of
data, in the database or associated with the system, by multiple
and unrelated owners of the data sets. For example, a first data
set which may be stored may be provided by a first party, a second
data set which may be stored may be provided by an unrelated second
party, and yet a third data set which may be stored, may be
provided by an third party unrelated to the first and second party.
Each of these three exemplary data sets may contain different
information that is stored using different data storage formats
and/or techniques. Further, each data set may contain subsets of
data that also may be distinct from other subsets.
As stated above, in various embodiments, the data can be stored
without regard to a common format. However, the data set (e.g.,
BLOB) may be annotated in a standard manner when provided for
manipulating the data in the database or system. The annotation may
comprise a short header, trailer, or other appropriate indicator
related to each data set that is configured to convey information
useful in managing the various data sets. For example, the
annotation may be called a "condition header", "header", "trailer",
or "status", herein, and may comprise an indication of the status
of the data set or may include an identifier correlated to a
specific issuer or owner of the data. In one example, the first
three bytes of each data set BLOB may be configured or configurable
to indicate the status of that particular data set; e.g., LOADED,
INITIALIZED, READY, BLOCKED, REMOVABLE, or DELETED. Subsequent
bytes of data may be used to indicate for example, the identity of
the issuer, user, transaction/membership account identifier or the
like. Each of these condition annotations are further discussed
herein.
The data set annotation may also be used for other types of status
information as well as various other purposes. For example, the
data set annotation may include security information establishing
access levels. The access levels may, for example, be configured to
permit only certain individuals, levels of employees, companies, or
other entities to access data sets, or to permit access to specific
data sets based on the transaction, merchant, issuer, user or the
like. Furthermore, the security information may restrict/permit
only certain actions such as accessing, modifying, and/or deleting
data sets. In one example, the data set annotation indicates that
only the data set owner or the user are permitted to delete a data
set, various identified users may be permitted to access the data
set for reading, and others are altogether excluded from accessing
the data set. However, other access restriction parameters may also
be used allowing various entities to access a data set with various
permission levels as appropriate.
The data, including the header or trailer may be received by a
standalone interaction device configured to add, delete, modify, or
augment the data in accordance with the header or trailer. As such,
in one embodiment, the header or trailer is not stored on the
transaction device along with the associated issuer-owned data but
instead the appropriate action may be taken by providing to the
user at the standalone device, the appropriate option for the
action to be taken. The system may contemplate a data storage
arrangement wherein the header or trailer, or header or trailer
history, of the data is stored on the system, device or transaction
instrument in relation to the appropriate data.
One skilled in the art will also appreciate that, for security
reasons, any databases, systems, devices, servers or other
components of the system may consist of any combination thereof at
a single location or at multiple locations, wherein each database
or system includes any of various suitable security features, such
as firewalls, access codes, encryption, decryption, compression,
decompression, and/or the like.
Encryption may be performed by way of any of the techniques now
available in the art or which may become available--e.g., Twofish,
RSA, El Gamal, Schorr signature, DSA, PGP, PKI, GPG (GnuPG), and
symmetric and asymmetric cryptosystems.
Firewall may include any hardware and/or software suitably
configured to protect CMS components and/or enterprise computing
resources from users of other networks. Further, a firewall may be
configured to limit or restrict access to various systems and
components behind the firewall for web clients connecting through a
web server. Firewall may reside in varying configurations including
Stateful Inspection, Proxy based, access control lists, and Packet
Filtering among others. Firewall may be integrated within a web
server or any other CMS components or may further reside as a
separate entity. A firewall may implement network address
translation ("NAT") and/or network address port translation
("NAPT"). A firewall may accommodate various tunneling protocols to
facilitate secure communications, such as those used in virtual
private networking. A firewall may implement a demilitarized zone
("DMZ") to facilitate communications with a public network such as
the Internet. A firewall may be integrated as software within an
Internet server, any other application server components or may
reside within another computing device or may take the form of a
standalone hardware component.
The computers discussed herein may provide a suitable website or
other Internet-based graphical user interface which is accessible
by users. In one embodiment, the MICROSOFT.RTM. INTERNET
INFORMATION SERVICES.RTM. (IIS), MICROSOFT.RTM. Transaction Server
(MTS), and MICROSOFT.RTM. SQL Server, are used in conjunction with
the MICROSOFT.RTM. operating system, MICROSOFT.RTM. NT web server
software, a MICROSOFT.RTM. SQL Server database system, and a
MICROSOFT.RTM. Commerce Server. Additionally, components such as
Access or MICROSOFT.RTM. SQL Server, ORACLE.RTM., Sybase, Informix
MySQL, Interbase, etc., may be used to provide an Active Data
Object (ADO) compliant database management system. In one
embodiment, the Apache web server is used in conjunction with a
Linux operating system, a My SQL database, and the Perl, PHP, Ruby,
and/or Python programming languages.
Any of the communications, inputs, storage, databases or displays
discussed herein may be facilitated through a website having web
pages. The term "web page" as it is used herein is not meant to
limit the type of documents and applications that might be used to
interact with the user. For example, a typical website might
include, in addition to standard HTML documents, various forms,
JAVA.RTM. applets, JAVASCRIPT, active server pages (ASP), common
gateway interface scripts (CGI), extensible markup language (XML),
dynamic HTML, cascading style sheets (CSS), AJAX (Asynchronous
JAVASCRIPT And XML), helper applications, plug-ins, and the like. A
server may include a web service that receives a request from a web
server, the request including a URL and an IP address
(123.56.789.234). The web server retrieves the appropriate web
pages and sends the data or applications for the web pages to the
IP address. Web services are applications that are capable of
interacting with other applications over a communications means,
such as the internet. Web services are typically based on standards
or protocols such as XML, SOAP, AJAX, WSDL and UDDI. Web services
methods are well known in the art, and are covered in many standard
texts. See, e.g., ALEX NGHIEM, IT WEB SERVICES: A ROADMAP FOR THE
ENTERPRISE (2003), hereby incorporated by reference. For example,
representational state transfer (REST), or RESTful, web services
may provide one way of enabling interoperability between
applications.
Middleware may include any hardware and/or software suitably
configured to facilitate communications and/or process transactions
between disparate computing systems. Middleware components are
commercially available and known in the art. Middleware may be
implemented through commercially available hardware and/or
software, through custom hardware and/or software components, or
through a combination thereof. Middleware may reside in a variety
of configurations and may exist as a standalone system or may be a
software component residing on the Internet server. Middleware may
be configured to process transactions between the various
components of an application server and any number of internal or
external systems for any of the purposes disclosed herein.
WEBSPHERE MQTM (formerly MQSeries) by IBM.RTM., Inc. (Armonk, N.Y.)
is an example of a commercially available middleware product. An
Enterprise Service Bus ("ESB") application is another example of
middleware.
Practitioners will also appreciate that there are a number of
methods for displaying data within a browser-based document. Data
may be represented as standard text or within a fixed list,
scrollable list, drop-down list, editable text field, fixed text
field, pop-up window, and the like. Likewise, there are a number of
methods available for modifying data in a web page such as, for
example, free text entry using a keyboard, selection of menu items,
check boxes, option boxes, and the like.
The system and method may be described herein in terms of
functional block components, screen shots, optional selections and
various processing steps. It should be appreciated that such
functional blocks may be realized by any number of hardware and/or
software components configured to perform the specified functions.
For example, the system may employ various integrated circuit
components, e.g., memory elements, processing elements, logic
elements, look-up tables, and the like, which may carry out a
variety of functions under the control of one or more
microprocessors or other control devices. Similarly, the software
elements of the system may be implemented with any programming or
scripting language such as C, C++, C #, JAVA.RTM., JAVASCRIPT,
JAVASCRIPT Object Notation (JSON), VBScript, Macromedia Cold
Fusion, COBOL, MICROSOFT.RTM. Active Server Pages, assembly, PERL,
PHP, awk, Python, Visual Basic, SQL Stored Procedures, PL/SQL, any
UNIX shell script, and extensible markup language (XML) with the
various algorithms being implemented with any combination of data
structures, objects, processes, routines or other programming
elements. Further, it should be noted that the system may employ
any number of conventional techniques for data transmission,
signaling, data processing, network control, and the like. Still
further, the system could be used to detect or prevent security
issues with a client-side scripting language, such as JAVASCRIPT,
VBScript or the like. For a basic introduction of cryptography and
network security, see any of the following references: (1) "Applied
Cryptography: Protocols, Algorithms, And Source Code In C," by
Bruce Schneier, published by John Wiley & Sons (second edition,
1995); (2) "JAVA.RTM. Cryptography" by Jonathan Knudson, published
by O'Reilly & Associates (1998); (3) "Cryptography &
Network Security: Principles & Practice" by William Stallings,
published by Prentice Hall; all of which are hereby incorporated by
reference.
In various embodiments, the software elements of the system may
also be implemented using Node.js.RTM.. Node.js.RTM. may implement
several modules to handle various core functionalities. For
example, a package management module, such as Npm.RTM., may be
implemented as an open source library to aid in organizing the
installation and management of third-party Node.js.RTM. programs.
Node.js.RTM. may also implement a process manager, such as, for
example, Parallel Multithreaded Machine ("PM2"); a resource and
performance monitoring tool, such as, for example, Node Application
Metrics ("appmetrics"); a library module for building user
interfaces, such as for example ReachJS.RTM.; and/or any other
suitable and/or desired module.
As used herein, the term "end user", "consumer", "customer",
"cardmember", "business" or "merchant" may be used interchangeably
with each other, and each shall mean any person, entity, government
organization, business, machine, hardware, and/or software. A bank
may be part of the system, but the bank may represent other types
of card issuing institutions, such as credit card companies, card
sponsoring companies, or third party issuers under contract with
financial institutions. It is further noted that other participants
may be involved in some phases of the transaction, such as an
intermediary settlement institution, but these participants are not
shown.
Each participant is equipped with a computing device in order to
interact with the system and facilitate online commerce
transactions. The customer has a computing unit in the form of a
personal computer, although other types of computing units may be
used including laptops, notebooks, hand held computers, set-top
boxes, cellular telephones, touch-tone telephones and the like. The
merchant has a computing unit implemented in the form of a
computer-server, although other implementations are contemplated by
the system. The bank has a computing center shown as a main frame
computer. However, the bank computing center may be implemented in
other forms, such as a mini-computer, a PC server, a network of
computers located in the same of different geographic locations, or
the like. Moreover, the system contemplates the use, sale or
distribution of any goods, services or information over any network
having similar functionality described herein
The merchant computer and the bank computer may be interconnected
via a second network, referred to as a payment network. The payment
network which may be part of certain transactions represents
existing proprietary networks that presently accommodate
transactions for credit cards, debit cards, and other types of
financial/banking cards. The payment network is a closed network
that is assumed to be secure from eavesdroppers. Exemplary
transaction networks may include the American Express.RTM.,
VisaNet.RTM., Veriphone.RTM., Discover Card.RTM., PayPal.RTM.,
ApplePay.RTM., GooglePay.RTM., private networks (e.g., department
store networks), and/or any other payment networks.
The electronic commerce system may be implemented at the customer
and issuing bank. In an exemplary implementation, the electronic
commerce system is implemented as computer software modules loaded
onto the customer computer and the banking computing center. The
merchant computer does not require any additional software to
participate in the online commerce transactions supported by the
online commerce system.
As will be appreciated by one of ordinary skill in the art, the
system may be embodied as a customization of an existing system, an
add-on product, a processing apparatus executing upgraded software,
a stand alone system, a distributed system, a method, a data
processing system, a device for data processing, and/or a computer
program product. Accordingly, any portion of the system or a module
may take the form of a processing apparatus executing code, an
internet based embodiment, an entirely hardware embodiment, or an
embodiment combining aspects of the internet, software and
hardware. Furthermore, the system may take the form of a computer
program product on a computer-readable storage medium having
computer-readable program code means embodied in the storage
medium. Any suitable computer-readable storage medium may be
utilized, including hard disks, CD-ROM, optical storage devices,
magnetic storage devices, and/or the like.
The system and method is described herein with reference to screen
shots, block diagrams and flowchart illustrations of methods,
apparatus (e.g., systems), and computer program products according
to various embodiments. It will be understood that each functional
block of the block diagrams and the flowchart illustrations, and
combinations of functional blocks in the block diagrams and
flowchart illustrations, respectively, can be implemented by
computer program instructions.
These computer program instructions may be loaded onto a general
purpose computer, special purpose computer, or other programmable
data processing apparatus to produce a machine, such that the
instructions that execute on the computer or other programmable
data processing apparatus create means for implementing the
functions specified in the flowchart block or blocks. These
computer program instructions may also be stored in a
computer-readable memory that can direct a computer or other
programmable data processing apparatus to function in a particular
manner, such that the instructions stored in the computer-readable
memory produce an article of manufacture including instruction
means which implement the function specified in the flowchart block
or blocks. The computer program instructions may also be loaded
onto a computer or other programmable data processing apparatus to
cause a series of operational steps to be performed on the computer
or other programmable apparatus to produce a computer-implemented
process such that the instructions which execute on the computer or
other programmable apparatus provide steps for implementing the
functions specified in the flowchart block or blocks.
Accordingly, functional blocks of the block diagrams and flowchart
illustrations support combinations of means for performing the
specified functions, combinations of steps for performing the
specified functions, and program instruction means for performing
the specified functions. It will also be understood that each
functional block of the block diagrams and flowchart illustrations,
and combinations of functional blocks in the block diagrams and
flowchart illustrations, can be implemented by either special
purpose hardware-based computer systems which perform the specified
functions or steps, or suitable combinations of special purpose
hardware and computer instructions. Further, illustrations of the
process flows and the descriptions thereof may make reference to
user WINDOWS.RTM., webpages, websites, web forms, prompts, etc.
Practitioners will appreciate that the illustrated steps described
herein may comprise in any number of configurations including the
use of WINDOWS.RTM., webpages, web forms, popup WINDOWS.RTM.,
prompts and the like. It should be further appreciated that the
multiple steps as illustrated and described may be combined into
single webpages and/or WINDOWS.RTM. but have been expanded for the
sake of simplicity. In other cases, steps illustrated and described
as single process steps may be separated into multiple webpages
and/or WINDOWS.RTM. but have been combined for simplicity.
The term "non-transitory" is to be understood to remove only
propagating transitory signals per se from the claim scope and does
not relinquish rights to all standard computer-readable media that
are not only propagating transitory signals per se. Stated another
way, the meaning of the term "non-transitory computer-readable
medium" and "non-transitory computer-readable storage medium"
should be construed to exclude only those types of transitory
computer-readable media which were found in In Re Nuijten to fall
outside the scope of patentable subject matter under 35 U.S.C.
.sctn. 101.
Benefits, other advantages, and solutions to problems have been
described herein with regard to specific embodiments. However, the
benefits, advantages, solutions to problems, and any elements that
may cause any benefit, advantage, or solution to occur or become
more pronounced are not to be construed as critical, required, or
essential features or elements of the disclosure. The scope of the
disclosure is accordingly to be limited by nothing other than the
appended claims, in which reference to an element in the singular
is not intended to mean "one and only one" unless explicitly so
stated, but rather "one or more." Moreover, where a phrase similar
to `at least one of A, B, and C` or `at least one of A, B, or C` is
used in the claims or specification, it is intended that the phrase
be interpreted to mean that A alone may be present in an
embodiment, B alone may be present in an embodiment, C alone may be
present in an embodiment, or that any combination of the elements
A, B and C may be present in a single embodiment; for example, A
and B, A and C, B and C, or A and B and C. Although the disclosure
includes a method, it is contemplated that it may be embodied as
computer program instructions on a tangible computer-readable
carrier, such as a magnetic or optical memory or a magnetic or
optical disk. All structural, chemical, and functional equivalents
to the elements of the above-described various embodiments that are
known to those of ordinary skill in the art are expressly
incorporated herein by reference and are intended to be encompassed
by the present claims. Moreover, it is not necessary for a device
or method to address each and every problem sought to be solved by
the present disclosure, for it to be encompassed by the present
claims. Furthermore, no element, component, or method step in the
present disclosure is intended to be dedicated to the public
regardless of whether the element, component, or method step is
explicitly recited in the claims. No claim element is intended to
invoke 35 U.S.C. 112(f) unless the element is expressly recited
using the phrase "means for." As used herein, the terms
"comprises", "comprising", or any other variation thereof, are
intended to cover a non-exclusive inclusion, such that a process,
method, article, or apparatus that comprises a list of elements
does not include only those elements but may include other elements
not expressly listed or inherent to such process, method, article,
or apparatus.
In yet another embodiment, the transponder, transponder-reader,
and/or transponder-reader system are configured with a biometric
security system that may be used for providing biometrics as a
secondary form of identification. The biometric security system may
include a transponder and a reader communicating with the system.
The biometric security system also may include a biometric sensor
that detects biometric samples and a device for verifying biometric
samples. The biometric security system may be configured with one
or more biometric scanners, processors and/or systems. A biometric
system may include one or more technologies, or any portion
thereof, such as, for example, recognition of a biometric. As used
herein, a biometric may include a user's voice, fingerprint,
facial, ear, signature, vascular patterns, DNA sampling, hand
geometry, sound, olfactory, keystroke/typing, iris, retinal or any
other biometric relating to recognition based upon any body part,
function, system, attribute and/or other characteristic, or any
portion thereof.
Phrases and terms similar to a "party" may include any individual,
consumer, customer, group, business, organization, government
entity, transaction account issuer or processor (e.g., credit,
charge, etc), merchant, consortium of merchants, account holder,
charitable organization, software, hardware, and/or any other type
of entity. The terms "user," "consumer," "purchaser," and/or the
plural form of these terms are used interchangeably throughout
herein to refer to those persons or entities that are alleged to be
authorized to use a transaction account.
Phrases and terms similar to "account", "account number", "account
code" or "consumer account" as used herein, may include any device,
code (e.g., one or more of an authorization/access code, personal
identification number ("PIN"), Internet code, other identification
code, and/or the like), number, letter, symbol, digital
certificate, smart chip, digital signal, analog signal, biometric
or other identifier/indicia suitably configured to allow the
consumer to access, interact with or communicate with the system.
The account number may optionally be located on or associated with
a rewards account, charge account, credit account, debit account,
prepaid account, telephone card, embossed card, smart card,
magnetic stripe card, bar code card, transponder, radio frequency
card or an associated account.
The system may include or interface with any of the foregoing
accounts, devices, and/or a transponder and reader (e.g. RFID
reader) in RF communication with the transponder (which may include
a fob), or communications between an initiator and a target enabled
by near field communications (NFC). Typical devices may include,
for example, a key ring, tag, card, cell phone, wristwatch or any
such form capable of being presented for interrogation. Moreover,
the system, computing unit or device discussed herein may include a
"pervasive computing device," which may include a traditionally
non-computerized device that is embedded with a computing unit.
Examples may include watches, Internet enabled kitchen appliances,
restaurant tables embedded with RF readers, wallets or purses with
imbedded transponders, etc. Furthermore, a device or financial
transaction instrument may have electronic and communications
functionality enabled, for example, by: a network of electronic
circuitry that is printed or otherwise incorporated onto or within
the transaction instrument (and typically referred to as a "smart
card"); a fob having a transponder and an RFID reader; and/or near
field communication (NFC) technologies. For more information
regarding NFC, refer to the following specifications all of which
are incorporated by reference herein: ISO/IEC 18092/ECMA-340, Near
Field Communication Interface and Protocol-1 (NFCIP-1); ISO/IEC
21481/ECMA-352, Near Field Communication Interface and Protocol-2
(NFCIP-2); and EMV 4.2 available at
http://www.emvco.com/default.aspx.
The account number may be distributed and stored in any form of
plastic, electronic, magnetic, radio frequency, wireless, audio
and/or optical device capable of transmitting or downloading data
from itself to a second device. A consumer account number may be,
for example, a sixteen-digit account number, although each credit
provider has its own numbering system, such as the fifteen-digit
numbering system used by American Express. Each company's account
numbers comply with that company's standardized format such that
the company using a fifteen-digit format will generally use
three-spaced sets of numbers, as represented by the number "0000
000000 00000". The first five to seven digits are reserved for
processing purposes and identify the issuing bank, account type,
etc. In this example, the last (fifteenth) digit is used as a sum
check for the fifteen digit number. The intermediary
eight-to-eleven digits are used to uniquely identify the consumer.
A merchant account number may be, for example, any number or
alpha-numeric characters that identify a particular merchant for
purposes of account acceptance, account reconciliation, reporting,
or the like.
In various embodiments, an account number may identify a consumer.
In addition, in various embodiments, a consumer may be identified
by a variety of identifiers, including, for example, an email
address, a telephone number, a cookie id, a radio frequency
identifier (RFID), a biometric, and the like.
Phrases and terms similar to "financial institution" or
"transaction account issuer" may include any entity that offers
transaction account services. Although often referred to as a
"financial institution," the financial institution may represent
any type of bank, lender or other type of account issuing
institution, such as credit card companies, card sponsoring
companies, or third party issuers under contract with financial
institutions. It is further noted that other participants may be
involved in some phases of the transaction, such as an intermediary
settlement institution.
Phrases and terms similar to "business" or "merchant" may be used
interchangeably with each other and shall mean any person, entity,
distributor system, software and/or hardware that is a provider,
broker and/or any other entity in the distribution chain of goods
or services. For example, a merchant may be a grocery store, a
retail store, a travel agency, a service provider, an on-line
merchant or the like.
The terms "payment vehicle," "financial transaction instrument,"
"transaction instrument" and/or the plural form of these terms may
be used interchangeably throughout to refer to a financial
instrument.
Phrases and terms similar to "merchant," "supplier" or "seller" may
include any entity that receives payment or other consideration.
For example, a supplier may request payment for goods sold to a
buyer who holds an account with a transaction account issuer.
Phrases and terms similar to a "buyer" may include any entity that
receives goods or services in exchange for consideration (e.g.
financial payment). For example, a buyer may purchase, lease, rent,
barter or otherwise obtain goods from a supplier and pay the
supplier using a transaction account.
Phrases and terms similar to "internal data" may include any data a
credit issuer possesses or acquires pertaining to a particular
consumer. Internal data may be gathered before, during, or after a
relationship between the credit issuer and the transaction account
holder (e.g., the consumer or buyer). Such data may include
consumer demographic data. Consumer demographic data includes any
data pertaining to a consumer. Consumer demographic data may
include consumer name, address, telephone number, email address,
employer and social security number. Consumer transactional data is
any data pertaining to the particular transactions in which a
consumer engages during any given time period. Consumer
transactional data may include, for example, transaction amount,
transaction time, transaction vendor/merchant, and transaction
vendor/merchant location. Transaction vendor/merchant location may
contain a high degree of specificity to a vendor/merchant. For
example, transaction vendor/merchant location may include a
particular gasoline filing station in a particular postal code
located at a particular cross section or address. Also, for
example, transaction vendor/merchant location may include a
particular web address, such as a Uniform Resource Locator ("URL"),
an email address and/or an Internet Protocol ("IP") address for a
vendor/merchant. Transaction vendor/merchant, and transaction
vendor/merchant location may be associated with a particular
consumer and further associated with sets of consumers. Consumer
payment data includes any data pertaining to a consumer's history
of paying debt obligations. Consumer payment data may include
consumer payment dates, payment amounts, balance amount, and credit
limit. Internal data may further comprise records of consumer
service calls, complaints, requests for credit line increases,
questions, and comments. A record of a consumer service call
includes, for example, date of call, reason for call, and any
transcript or summary of the actual call.
Phrases similar to a "payment processor" may include a company
(e.g., a third party) appointed (e.g., by a merchant) to handle
transactions. A payment processor may include an issuer, acquirer,
authorizer and/or any other system or entity involved in the
transaction process. Payment processors may be broken down into two
types: front-end and back-end. Front-end payment processors have
connections to various transaction accounts and supply
authorization and settlement services to the merchant banks'
merchants. Back-end payment processors accept settlements from
front-end payment processors and, via The Federal Reserve Bank,
move money from an issuing bank to the merchant bank. In an
operation that will usually take a few seconds, the payment
processor will both check the details received by forwarding the
details to the respective account's issuing bank or card
association for verification, and may carry out a series of
anti-fraud measures against the transaction. Additional parameters,
including the account's country of issue and its previous payment
history, may be used to gauge the probability of the transaction
being approved. In response to the payment processor receiving
confirmation that the transaction account details have been
verified, the information may be relayed back to the merchant, who
will then complete the payment transaction. In response to the
verification being denied, the payment processor relays the
information to the merchant, who may then decline the
transaction.
Phrases similar to a "payment gateway" or "gateway" may include an
application service provider service that authorizes payments for
e-businesses, online retailers, and/or traditional brick and mortar
merchants. The gateway may be the equivalent of a physical point of
sale terminal located in most retail outlets. A payment gateway may
protect transaction account details by encrypting sensitive
information, such as transaction account numbers, to ensure that
information passes securely between the customer and the merchant
and also between merchant and payment processor.
* * * * *
References